Cognition as Morphological/Morphogenetic Embodied Computation In Vivo

Abstract

Cognition, historically considered uniquely human capacity, has been recently found to be the ability of all living organisms, from single cells and up. This study approaches cognition from an info-computational stance, in which structures in nature are seen as information, and processes (information dynamics) are seen as computation, from the perspective of a cognizing agent. Cognition is understood as a network of concurrent morphological/morphogenetic computations unfolding as a result of self-assembly, self-organization, and autopoiesis of physical, chemical, and biological agents. The present-day human-centric view of cognition still prevailing in major encyclopedias has a variety of open problems. This article considers recent research about morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, free energy principle, cognition as Bayesian learning, active inference, and related topics, offering new theoretical and practical perspectives on problems inherent to the old computationalist cognitive models which were based on abstract symbol processing, and unaware of actual physical constraints and affordances of the embodiment of cognizing agents. A better understanding of cognition is centrally important for future artificial intelligence, robotics, medicine, and related fields.

Machine learning for hypothesis generation in biology and medicine: exploring the latent space of neuroscience and developmental bioelectricity

“…This is a very interesting translation, because it suggests the use of a predictive coding model (a predictive gradient) to understand progressive morphogenesis. 88,89 This is testable, as biophysical or biochemical representations of cell expectations (homeostatic setpoints, in the sense of priors [90][91][92][93][94][95][96] ) can be looked for experimentally.…”

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine

“…The non-neural bioelectricity framework, 47 , 220 , 260 together with perspectives from the diverse intelligence and computer science communities, 42 , 261 , 262 are revealing a biomedical roadmap that is not stuck at the molecular level any more than modern cognitive science is exclusively focused on synaptic machinery. This will not only drive the design of much more effective interventions for regenerative medicine but will also advance neuroscience (by solving key problems in simpler, evolutionarily more basal, contexts), enable better control of complex morphogenesis for bioengineered synthetic constructs (such as novel biorobotics), and even the field of evolutionary developmental biology (by improving our understand of the relationship between the genetically specified hardware and the phenotypes that result from the physiological software that drives biological robustness and competency).…”

Machine Learning for Hypothesis Generation in Biology and Medicine: Exploring the latent space of neuroscience and developmental bioelectricity

“…Taken together, this demonstrates how morphogenetic gradients in the organizer region are used and updated flexibly for directing cellular behaviors during development. This is a very interesting translation, because it suggests the use of a predictive coding model (a predictive gradient) to understand progressive morphogenesis [85,86]. This is testable, as biophysical or biochemical representations of cell expectations (homeostatic setpoints, in the sense of priors [87][88][89][90][91][92][93]) can be looked for experimentally.…”